Abstract: A method, and system, of digital room correction for a device, such as a smart speaker, including a loudspeaker. The method comprises capturing audio from an environment local to the device, for example from one or more microphones of a smart speaker. The captured audio is then processed to recognize one or more categories of sound. A digital room correction procedure may then be controlled dependent upon recognition and/or analysis of at least one of the categories of sound.

Abstract: A method of training a non-verbal sound class detection machine learning system, the non-verbal sound class detection machine learning system comprising a machine learning model configured to: receive data for each frame of a sequence of frames of audio data obtained from an audio signal; for each frame of the sequence of frames: process the data for multiple frames; and output data for at least one sound class score representative of a degree of affiliation of the frame with at least one sound class of a plurality of sound classes, wherein the plurality of sound classes comprises: one or more target sound classes; and a non-target sound class representative of an absence of each of the one or more target sound classes; wherein the method comprises: training the machine learning model using a loss function.

Abstract: A mobile device comprising a software application configured to detect the sound of a product use event; provide a user reward using said software application in response to said detection; capture data relating to said product use event; and provide said captured data to a remote computer system for analysis.

Abstract: Broadly speaking, embodiments of the present invention provide a device, systems and methods for capturing sounds, generating a sound model (or “sound pack”) for each captured sound, and identifying a detected sound using the sound model(s). Preferably, a single device is used to capture a sound, store sound models, and to identify a detected sound using the stored sound models.

Abstract: A mobile device comprising a software application configured to detect the sound of a product use event; provide a user reward using said software application in response to said detection; capture data relating to said product use event; and provide said captured data to a remote computer system for analysis.

Abstract: A digital sound identification system for storing a Markov model is disclosed. A processor is coupled to a sound data input, working memory, and a stored program memory for executing processor control code to input sound data for a sound to be identified. The sample sound data defines a sample frequency domain data energy in a range of frequency. Mean and variance values for a Markov model of the sample sound are generated. The Markov model is stored in the non-volatile memory. Interference sound data defining interference frequency domain data is inputted. The mean and variance values of the Markov model using the interference frequency domain data are adjusted. Sound data defining other sound frequency domain data are inputted. A probability of the other sound frequency domain data fitting the Markov model is determined. Finally, sound identification data dependent on the probability is outputted.

Abstract: A digital sound identification system for storing a Markov model is disclosed. A processor is coupled to a sound data input, working memory, and a stored program memory for executing processor control code to input sound data for a sound to be identified. The sample sound data defines a sample frequency domain data energy in a range of frequency. Mean and variance values for a Markov model of the sample sound are generated. The Markov model is stored in the non-volatile memory. Interference sound data defining interference frequency domain data is inputted. The mean and variance values of the Markov model using the interference frequency domain data are adjusted. Sound data defining other sound frequency domain data are inputted. A probability of the other sound frequency domain data fitting the Markov model is determined. Finally, sound identification data dependent on the probability is outputted.

Abstract: We describe a digital sound identification system, the system comprising: non-volatile memory for storing a Markov model; stored program memory storing processor control code; a sound data input; a processor coupled to said sound data input, to said working memory, and to said stored program memory for executing said processor control code, and wherein said processor control code comprises code to: input, from said sound data input, first sample sound data for a first sound to be identified, said first sample sound data defining first sample frequency domain data, said first sample frequency domain data defining an energy of said first sample in a plurality of frequency ranges; generate a first set of mean and variance values for at least a first Markov model of said first sample sound from said first sample frequency domain data; store said first Markov model in said non-volatile memory; input interference sound data defining interference frequency domain data; adjust said mean and variance values of said fi